---
title: "Flex Dashboards for Sharing Results"
output:
  flexdashboard::flex_dashboard:
    source_code: embed
    orientation: columns
    theme: cosmo
editor_options: 
  chunk_output_type: console
---

```{r, libraries, include = FALSE}
## here to navigate project directory
library(here)

## tidyverse for data wrangling and visualization
library(tidyverse)

## snakecase for naming conventions
library(snakecase)

## flexdashboard to build dashboards in R Markdown
library(flexdashboard)

## shiny for reactive dashboards
library(shiny)

## jsonlite to import json data files
library(jsonlite)

## hexbin for hex plots
library(hexbin)

## DT for web-friendly, interactive tables
library(DT)
```

```{r, global, include = FALSE}
airline <- read_json(here("data","airline.json"), simplifyVector = TRUE)

# Convert the table to a tibble
airline <- as_tibble(airline)

# Update the table as described
airline <- airline %>%
  mutate(across(.cols = where(is.character), .fns = as.factor),
    Class = fct_relevel(Class, "Business", after = 2)) %>%
  
  rename_with(.fn = to_snake_case)


print(airline)


```

Column
-----------------------------------------------------------------------

### Recommendation Violin Plot by Customer Type and Class

```{r, plot_1}
# Create the violin plot
violin_plot <- ggplot(airline, aes(x = customer_type, y = recommendation, fill = class)) +
  geom_violin() +
  labs(x = "Customer Type", y = "Recommendation", fill = "Class")

# Print the plot
print(violin_plot)
```

### Count of Recommendation and Age 

```{r, plot_2}
hexbin_plot <- ggplot(airline, aes(x = age, y = recommendation)) +
  geom_hex() +
  labs(x = "Age", y = "Recommendation", fill = "Count")

# Print the plot
print(hexbin_plot)
```

Column 
-----------------------------------------------------------------------

### Recommendation Heat Map for Leg Room and Cleanliness

```{r, plot_3}
# Create the tile plot
tile_plot <- ggplot(airline, aes(x = leg_room, y = cleanliness, fill = recommendation)) +
  geom_tile() +
  scale_fill_gradient2(low = "blue", mid = "white", high = "red", midpoint = 50) +
  labs(x = "Leg Room", y = "Cleanliness", fill = "Recommendation")

# Print the plot
print(tile_plot)
```

### Recommendation and Flight Distance 2D Density by Customer Type and Class

```{r, plot_4}
# Create the 2D density plot
density_plot <- ggplot(airline, aes(x = flight_distance, y = recommendation)) +
  geom_density_2d_filled(contour_var = "ndensity") +
  facet_grid(customer_type ~ class) +
  labs(x = "Flight Distance", y = "Recommendation", fill = "Density")

# Print the plot
print(density_plot)
```